9 Use Cases for AI in Last-Mile Delivery
Wise Systems
Artificial intelligence (AI) is developing rapidly. With it, the number of potential use cases for AI in last-mile delivery—the final step of the delivery process that brings the product to the consumer’s doorstep—continues to grow.
AI technology is vital for companies that are faced with an increasing number and volume of deliveries due to significant growth in e-commerce and B2C (business-to-consumer) trade. For example, B2C trade accounted for 10-15% of DHL’s volume a decade ago, growing to 40% of its volume by September 2023, according to Oliver Facey, Senior Vice President of Global Network Operations Programs at DHL Express.
AI plays a crucial role in overcoming last-mile delivery challenges by enhancing efficiency, accuracy, and customer satisfaction in last-mile delivery. In this post, we’ll discuss some of the most common and significant use cases for AI in last-mile delivery.
In this article:
- Route Optimization
- Dynamic Scheduling and Capacity Planning
- Address Validation and Correction
- Predictive Analytics for Accurate Delivery Times
- Real-Time Tracking and Updates
- Autonomous Delivery Vehicles
- Load Optimization
- Customer Service Optimization
- Sustainability Initiatives
- Final Thoughts
- Frequently Asked Questions
Route Optimization
Route optimization software leverages AI algorithms to analyze traffic data, weather conditions, and road closures in real-time to determine the most efficient delivery routes.
AI-powered route optimization tools replace the previous complex and time-consuming manual route creation process, saving dispatchers valuable time while also reducing delivery times and minimizing fuel consumption and operational costs.
Dynamic Scheduling and Capacity Planning
AI systems can forecast delivery volume fluctuations and help allocate resources more effectively. This includes optimizing delivery personnel schedules and vehicle usage to match anticipated demand.
AI algorithms can adjust delivery schedules in real-time based on changing conditions such as traffic, weather, or last-minute customer requests. This flexibility ensures that deliveries remain efficient and timely, even when unexpected changes occur.
AI systems can also account for customer preferences, such as preferred delivery times, and dynamically schedule deliveries to meet these expectations, enhancing customer satisfaction.
Address Validation and Correction
AI tools can automatically validate and correct addresses during the order process. AI algorithms are trained to recognize patterns in address data, enabling them to validate addresses by comparing input data against a comprehensive, constantly updated database of valid addresses, helping to prevent packages from being sent to incorrect or non-existent addresses.
AI systems can also identify errors such as misspellings, incorrect postal codes, and missing information. Over time, AI models learn from corrections and successful deliveries, improving their ability to predict and correct address errors automatically. This reduces the risk of failed deliveries due to address errors and ensures a smoother delivery process.
Predictive Analytics for Accurate Delivery Times
By identifying patterns and trends in historical delivery data, AI models can predict how long future deliveries will take under similar circumstances. AI considers historical trends related to specific times of the day, days of the week, and seasons, which helps account for variations in traffic, weather conditions, and delivery volumes, which can affect delivery times.
AI systems also integrate real-time data on traffic congestion, road closures, and weather conditions to predict potential delays due to unexpected events, such as accidents or severe weather, and adjust predicted delivery times dynamically to improve on-time delivery.
Real-Time Tracking and Updates
AI systems integrate data from various sources, including GPS trackers, vehicle telematics, and traffic and weather reports, to provide a comprehensive view of the delivery process in real-time and provide customers and businesses with real-time updates on the location of their deliveries.
This transparency improves customer trust and reduces the need for customer service inquiries. It’s also helpful for delivery companies, enabling them to manage and track a large volume of deliveries simultaneously, improving scalability and operational efficiency.
Autonomous Delivery Vehicles
AI is increasingly used to power autonomous drones and ground vehicles for package delivery, particularly useful in hard-to-reach areas or to enhance speed and reduce human labor costs. These vehicles use AI for navigation, obstacle avoidance, and safe delivery of goods.
Some autonomous delivery vehicles offer advanced AI-driven capabilities such as automated loading and unloading or smart locker integration.
Load Optimization
AI algorithms can optimize how packages are loaded into delivery vehicles, ensuring that space is used efficiently and the order of deliveries minimizes the need for rearrangement. This ensures that each delivery vehicle is loaded to its optimal capacity, reducing the number of trips required to deliver the same amount of goods. This decreases the overall fuel consumption and emissions associated with delivery operations.
Customer Service Optimization
AI significantly enhances customer service in last-mile delivery through several innovative approaches that streamline interactions, provide personalized experiences, and increase overall satisfaction. We’ve already touched on some of these approaches above, but AI can improve customer service in many other ways.
For example, AI-powered chatbots and virtual assistants provide instant responses to customer inquiries around the clock. These tools can handle a wide range of queries, from tracking deliveries to processing returns or exchanges, reducing wait times and improving the customer experience.
AI can also analyze customer data, including past orders, preferences, and interactions, to offer personalized recommendations and services. For instance, it can suggest convenient delivery times or locations based on the customer’s history.
Sustainability Initiatives
AI can help companies reduce their carbon footprint by optimizing routes and delivery operations, contributing to more sustainable delivery practices.
For instance, AI algorithms ensure that each delivery vehicle is loaded to its optimal capacity, reducing the number of trips required to deliver the same amount of goods. This decreases the overall fuel consumption and emissions associated with delivery operations. It can also offer customers sustainable delivery choices, such as grouping deliveries to reduce trips or selecting eco-friendly packaging.
Final Thoughts
Tune in to our latest Feature Spotlight to discover how simplified route visualizations and real-time fleet visibility optimize your last-mile. Schedule a demo with us today to learn more about Dispatcher from Wise Systems here! https://t.co/dMPjyYSEZv pic.twitter.com/u1kpYbKkd6
— Wise Systems (@goWiseSystems) April 17, 2023
AI impacts many last-mile delivery processes, from automating complex decision-making processes to enhancing efficiency through intelligent automation, improving forecasting and inventory management accuracy, enabling real-time visibility across the supply chain, and facilitating personalized customer experiences. AI also plays a vital role in innovation, such as autonomous vehicles and drones, which will also redefine logistics operations and strategies.
AI once came at a high cost. Coupled with the perceived complexity of implementing AI systems, some organizations opted not to adopt AI technologies when they first emerged. Today, AI systems like the Wise Systems delivery automation platform offer streamlined implementation and a variety of solutions to help companies optimize last-mile delivery, cut costs, and quickly achieve an ROI on their investment.
Request a demo today to discover how our AI-powered routing and planning engine can help you achieve your last-mile delivery KPIs.
Frequently Asked Questions
How is AI used in last-mile delivery?
AI is used in last-mile delivery through route optimization, predictive analytics for delivery times, autonomous delivery vehicles, dynamic scheduling, personalized customer experiences, real-time tracking and updates, address validation, fraud detection, and load optimization. These applications aim to make the delivery process more efficient, accurate, and customer-centric.
What is the new technology in last-mile delivery?
New technologies in last-mile delivery include autonomous delivery robots and drones, electric and autonomous vehicles, advanced data analytics, IoT devices for real-time tracking, and smart lockers for secure and flexible package drop-off. These innovations are designed to address challenges related to speed, cost, and scalability in the last mile.
What’s the biggest issue with last-mile delivery?
The biggest issue with last-mile delivery is its high cost, accounting for a significant portion of the total shipping cost due to inefficiencies, route complexity, and the need for individualized deliveries. Other challenges include meeting rising customer expectations for fast and free delivery, managing variable demand, and ensuring delivery accuracy and safety.
What are the benefits of AI in logistics?
The benefits of AI in logistics include enhanced operational efficiency through optimized routes and schedules, improved demand forecasting, increased speed and accuracy of deliveries, reduced operational costs, and better customer service through personalized experiences and real-time communication.
